Gene expression informatics with an automatic histogram-type membership function for non-uniform data

Akito Daiba, Satoru Ito, Tsutomu Takeuchi, Masafumi Yohda

Research output: Contribution to journalArticle

Abstract

The non-uniformity of gene expression data is one of the factors that make gene expression analysis difficult. Gene expression data often do not follow a normal distribution but rather various distributions within each group. Thus, it is impossible to apply basic statistical techniques such as the t-test. In this study, we have developed an analysis method for gene expression data obtained by microarrays using a fuzzy logic algorithm with original membership functions. The method automatically evaluates the data from a histogram of gene expression information for a patient group. Using this method, we predicted the efficacy of an anti-TNF-α treatment for rheumatoid arthritis. We created a prediction model for the effects of 14 weeks of anti-TNF-α treatment based on the gene expression data from the peripheral blood of rheumatoid arthritis patients before the treatment. The model had a predictive success of 89% in the model-establishing data group, 94% in the training group, and 89% in the validation group. The results suggest that the method presented here could be an extremely effective tool for gene expression analysis.

Original languageEnglish
Pages (from-to)13-23
Number of pages11
JournalChem-Bio Informatics Journal
Volume10
Issue number1
Publication statusPublished - 2010

Fingerprint

Informatics
Membership functions
Gene expression
Gene Expression
Rheumatoid Arthritis
Fuzzy Logic
Normal Distribution
Normal distribution
Microarrays
Fuzzy logic
Blood
Therapeutics

Keywords

  • Fuzzy logic
  • Gene expression
  • Microarray
  • Prediction of therapeutic efficacy
  • Rheumatoid arthritis

ASJC Scopus subject areas

  • Biochemistry

Cite this

Gene expression informatics with an automatic histogram-type membership function for non-uniform data. / Daiba, Akito; Ito, Satoru; Takeuchi, Tsutomu; Yohda, Masafumi.

In: Chem-Bio Informatics Journal, Vol. 10, No. 1, 2010, p. 13-23.

Research output: Contribution to journalArticle

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